8 papers
Do LLMs Know What to Ask and When? Evaluating Multi-Turn Information Seeking
Yepeng Huang, Jiawen Zhang, Michelle Dai +4
When a user question is underspecified, a capable model should recognize that its context is insufficient, identify the missing information, ask for it, and respond only once that…
An AI agent for treatment reasoning over a biomedical tool universe
Shanghua Gao, Ayush Noori, Richard Zhu +13
Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an…
STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations
Boyang Fu, George Dasoulas, Sameer Gabbita +5
Predicting how genetic perturbations change cellular state is a core problem for building controllable models of gene regulation. Perturbations targeting the same gene can produce…
KnowGuard: Knowledge-Driven Abstention for Multi-Round Clinical Reasoning
Xilin Dang, Kexin Chen, Xiaorui Su +7
In clinical practice, physicians refrain from making decisions when patient information is insufficient. This behavior, known as abstention, is a critical safety mechanism preventi…
Multimodal AI predicts clinical outcomes of drug combinations from preclinical data
Yepeng Huang, Xiaorui Su, Varun Ullanat +10
Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations, reducing late-stage clinical failures, and accelerating the de…
Multimodal Medical Code Tokenizer
Xiaorui Su, Shvat Messica, Yepeng Huang +5
Foundation models trained on patient electronic health records (EHRs) require tokenizing medical data into sequences of discrete vocabulary items. Existing tokenizers treat medical…